Dr. Duangrudee TANRAMLUK
-Assistant Professer at Institute of Molecular Biosciences, Mahidol University, Thailand (2010-current) -Working Committee at Integrative Computational BioScience Center (ICBS), Mahidol University, Thailand (2011-current) -Group Leader at MANORAA & MproCOVID.com & Ligand.Cafe
Research Goals
To come up with algorithms that allow for binding affinity prediction from high resolution of protein X-ray crystallographic structure and linking databases to facilitate rationale drug design. Based on trends of large amount of distances, shape, and charge complementary in the homologous structure, we want to improve the small molecule drug binding to protein and provide platform as an extended brain for drug design researchers.What is the pain point that we offer solution?
Most computational methods do not rely on the real structural interpretation that are understandable by common sense. The computed results are mostly reported from docking and simulation rather than training from angstrom resolution pictures. Only by observing large amount of homologous protein-ligand pockets and reverse engineer them would allow us to dissect binding affinity and design molecules, either the ligand or the protein target, in a human interpretable way.
What is unique about our research?
Our research experiences in deeptech, ranging from quantum chemical calculations, protein X-ray crystallography, and software development, have shaped our vision and expertise on how to design tools to solve small molecule drug design problems based on high resolution X-ray structure of proteins. Key inventions on Manoraa.org systems can assist drug discovery by linking ligand to target proteins, baseline expression, SNPs, pathways, tissues, and organs as featured in Nucleic Acids Research (JIF 19). By linking these information in this Manoraa ligand design hub, researchers can perform in silico target discovery and ligand design before planing their wet lab experiments.
What are our success stories?
Our algorithms were proven experimentally and enabled us to compare the shape of the pocket, to observe position specific interactions, and to display chemical interactions inside the proteins. We proposed the three-dimensional pictures of imaginary drug based on Frequently Occurring Atoms for pan-variant inhibitor design. Our work was featured in Top 100 Chemistry from Nature Portfolio and Most Read article at Structure by Cell Press. The effects from parameters therein, such as the entities that usually participate in hydrogen bond formation, the parts of the pocket that expand or contract upon binding to inhibitors, can be displayed to guide scientists while formulating an inhibitor design strategy.
We can help drug design research sustainable by make the process faster, cheaper and more effective.
